In order to realize the overloading dynamic characteristics of shell comprehensively and objectively in the process of penetration into target, the non-stationary penetration acceleration signals must be processed. In the paper, the actual acceleration signal which was obtained in the test of projectile penetration into target is analyzed, such as pretreatment, integral analysis in the time domain, by Fourier transform in the field of frequency. This paper has a wavelet analysis on the signal, gets the characteristics of the penetration acceleration signal based on signal processing and the comprehensive analysis and provides a kind of data processing method to get the concerned data for engineers.
Based on the measured signals of extremely low frequency underwater magnetic targets, this paper extracts the magnetic anomaly data of sensors at 50Hz for denoising processing, and uses the denoised magnetic anomaly data as the basic data. Random noise is added to the basic data as a simulated noise signal. Based on Stein's unbiased likelihood estimation threshold method, two wavelet family Symlets, Daubechie and soft and hard threshold selection methods commonly used in denoising literature are used to denoise the measured underwater magnetic target signal data. The different de-noising results are calculated and compared. The de-noising signal-to-noise ratio of the data is used as the evaluation index, and the de-noising effect and reliability of different combined methods are objectively analyzed. Research shows that the hard-thresholding of sym7 as the generating wave function has the lowest signal-to-noise ratio and poor denoising effect. The soft threshold processing using db9 as the generating wave function has the highest signal-to-noise ratio, which can achieve the best denoising effect.
In recent years, vibration-based structural damage identification has made significant progress by exploiting data-driven deep learning techniques, which can efficiently extract damage-sensitive features from a large amount of data. However, in some practical engineering applications, large volumes of measurement data are not readily available. This paper proposes a novel physics-guided residual neural network (PhyResNet) framework to improve the robustness and accuracy of structural damage identification under data-scarce conditions. In contrast to the state-of-the-art purely data-driven ResNet, the proposed method embedded available physics knowledge (e.g., governing equations of dynamics) of structures into the feature learning process via a novel physics-based loss function. The input-output relationship of the network is constrained to retain its physical meaning implicitly while the demand for large amounts of labeled training data is reduced. Notably, even with only 5 % of the dataset used for training, PhyResNet achieves a 13.1 % improvement in R-Value. The performance of the proposed approach is evaluated through both numerical and experimental verifications. Results demonstrate that damage localization and quantification are achieved with high accuracies and good robustness.
Moving vehicles equipped with various types of sensors can efficiently monitor the health conditions of a population of transportation infrastructure such as bridges. This paper presents a mobile crowdsensing framework to identify dense spatial-resolution bridge mode shapes using sparse drive-by measurements. The proposed method converts mode shape identification into a physical-informed optimization problem with two objective function terms. The first objective minimises the mode shape identification error based on the fact that the ratio of a specific order mode shape value at any two locations is time-invariant. Since the bridge mode shape should be globally smooth even when the local stiffness is discontinuous, the smoothness of the identified mode shape is introduced as the second objective. The feasibility and advantages of the proposed model are verified numerically and through large-scale experimental studies. Numerical results demonstrate that the proposed method can efficiently identify bridge mode shapes with a desirable accuracy. The adverse effects of road roughness and measurement noise on the mode shape identification accuracy are substantially suppressed by introducing crowdsensing and making use of collected responses over multiple trips. The applicability of the proposed method for bridges having varying cross sections and multiple spans is also studied. A series of drive-by tests with different vehicle masses and speeds are conducted on a large-scale footbridge. The experimental results verify that the proposed method can accurately identify the bridge mode shapes and is robust to vehicle mass and speed variation. The identification accuracy of large-scale bridge mode shapes using crowdsensing drive-by measurements is demonstrated in this study.
This paper proposes a relative displacement sensor developed to measure directly the relative slip between slab and girder in composite bridges for assessing the health condition of shear connections. The structure, design principle, features, and calibration of the developed relative displacement sensor are presented. The design of the sensor ensures that there are no voltage outputs for the tension, compression, bending, and torsion effects, but only for the relative displacement between the two connecting pads of the sensor. The accuracy of the developed sensor in measuring the relative displacement response and using it for monitoring the conditions of shear connectors was tested on a composite bridge model in the laboratory. Shear connection condition was monitored under ambient vibrations, then static load tests were conducted to introduce cracks into the composite bridge. Both the vertical deflections and relative displacements were used for the crack detection. Experimental studies demonstrate that the developed sensor is very sensitive to the relative displacement and has a decent performance for the structural health monitoring of composite bridges. Copyright © 2014 John Wiley & Sons, Ltd.
The new-type power system has relatively low inertia due to the substantial replacement of synchronous generators (SGs) by converter-interfaced generators (CIGs). Low inertia may result in faster frequency dynamics and threaten the frequency stability of the new-type power system. This paper investigates the inertia response characteristic of typical devices in the new-type power system. By the analogy of the mathematical form of SG inertia, the inertia of asynchronous devices, such as asynchronous motors and CIGs with virtual synchronous generator (VSG) control, can be obtained. The analysis is significant for evaluating of inertia resources in the new-type power system.
In the face of global warming, reducing carbon dioxide emissions has become a common concern. Integrated energy system (IES) is considered to be a supporting technology to increase the proportion of clean energy use and achieve goals of carbon emission reduction and carbon neutrality. By introducing carbon neutral costs and demand response into scheduling strategy, the carbon emission and the economy of IES system operation is optimized in the proposed optimal scheduling. First, modeling of the demand response considering the transferable load and replaceable load, as well as reducible load, is established for guiding users to change their energy consumption ways. Then Structure and components of IES are discussed. Later, the article presents the IES carbon emissions calculation method and model of carbon neutrality cost. Finally, the day ahead optimal scheduling of the IES considering the demand response (DR) and carbon neutrality cost is proposed. The results show that the proposed model is effective in balancing low carbon and economy. Comparative results of different carbon neutralization adjustment coefficients shows that total operating cost as well as carbon emissions is reduced with increasing clean energy penetration and setting moderate carbon neutralization cost coefficients. By adjusting the participation of clean energy, the system cost can be effectively reduced by 48.6%. By adjusting the carbon neutralization cost coefficients, the overall carbon emission can be reduced by 282 kg.
The Xiazhang Sea-Crossing Bridge,totally 9. 333 km long,is composed of four major parts of the north bridge,Haimen Island interchange and toll service area,south bridge and Haiping interchange. To accommodate the complicated natural and construction conditions of the bridge,the bridge site schemes were compared and the bridge type schemes for the critical control projects ( the north and south main bridges) were studied. According to the study,it was finally determined that the 5-span continuous steel box girder cable-stayed bridge with a main span 780 m would be adopted for the north main bridge and the composite girder cable-stayed bridge with a main span 300 m would be adopted for the south main bridge. In consideration of the complicated geologic conditions,high intensity earthquake and harsh wind environment,the well adaptive pile foundations were respectively utilized. For the pile foundations at the locations with poor geology,the foundations would be grouted. The structures of the bridge were designed in compliance with the seismic resistance requirements. At the connections between the pylons and girders of the main bridges,the longitudinal dampers were arranged and on the piers of the approach bridges,the earthquake isolation bearings were arranged. For the north main bridge,a kind of the wing type balustrade was utilized while for the south main bridge,the wind resistant measure of the guide vanes was taken.
Mechanical distortions of phased array antennas make transmit pattern distort. The transmit pattern cannot be simply calibrated by compensating the position error of each element since the efiect of the mechanical distortions is angle-dependent. To solve this problem, we treat the element position errors measurement as prior knowledge and propose a knowledge-aided (potentially cognitive) transmit pattern design method. When the mechanical distortions occur, the cognitive transmit pattern can still place pattern nulls in the directions of interferences while preserving the main beam response of the target of interest. The proposed method is validated by simulation results.
